Generating ground truths for machine learning

ABSTRACT

A messaging system processes three-dimensional (3D) models to generate ground truths for training machine learning models for applications of the messaging system. A method of generating ground truths for machine learning includes generating a plurality of first rendered images from a first 3D base model where each first rendered image includes the 3D base model modified by first augmentations of a plurality of augmentations. The method further includes determining for a second 3D base model incompatible augmentations of the first plurality of augmentations, where the incompatible augmentations indicate changes to fixed features of the second 3D base model, and generating a plurality of second rendered images from a second 3D base model, each second rendered image comprising the second 3D base model modified by second augmentations, the second augmentations corresponding to the first augmentations of a corresponding first rendered image, where the second augmentations comprises augmentations of the first augmentations that are not incompatible augmentations.

TECHNICAL FIELD

Examples of the present disclosure relate generally to generating groundtruths for training machine learning (ML) models. More particularly, butnot by way of limitation, examples of the present disclosure relate togenerating ground truths of images where the images are based on athree-dimensional (3D) base model for the ground truth input and amodified 3D base model for the ground truth output, and whereaugmentations are applied to the 3D base models to ensure the groundtruths are diverse and inclusive.

BACKGROUND

Generating ground truths to train machine learning models such asconvolutional neural networks (CNNs) is time consuming and, often, theground truth inadequately trains the CNN to process the inputs so thatpoor quality outputs are obtained.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

In the drawings, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. To easily identifythe discussion of any particular element or act, the most significantdigit or digits in a reference number refer to the figure number inwhich that element is first introduced. Some examples are illustrated byway of example, and not limitation, in the figures of the accompanyingdrawings in which:

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexamples.

FIG. 2 is a diagrammatic representation of a messaging system, inaccordance with some examples, that has both client-side and server-sidefunctionality.

FIG. 3 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some examples.

FIG. 4 is a diagrammatic representation of a message, in accordance withsome examples.

FIG. 5 is a flowchart for an access-limiting process, in accordance withsome examples.

FIG. 6 illustrates generating ground truths for machine learning, inaccordance with some examples.

FIG. 7 illustrates augmentation database, in accordance with someexamples.

FIG. 8 illustrates a base model and a modified base model, in accordancewith some examples.

FIG. 9 illustrates different faces, in accordance with some examples.

FIG. 10 illustrates an example of rendered images and segmentation maps1000, in accordance with some examples.

FIG. 11 illustrates clothing augmentations, in accordance with someexamples.

FIG. 12 illustrates a proxy object, in accordance with some examples.

FIG. 13 illustrates a proxy object, in accordance with some examples.

FIG. 14 illustrates skin textures, in accordance with some examples.

FIG. 15 illustrates eye textures, in accordance with some examples.

FIG. 16 illustrates applying blendshape augmentations to a 3D basemodel, in accordance with some examples.

FIG. 17 illustrates High Dynamic Range Imagery (HDRI) lighting, inaccordance with some examples.

FIG. 18 illustrates HDRI lighting, in accordance with some examples.

FIG. 19 illustrates rendered images with different lighting, inaccordance with some examples.

FIG. 20 illustrates the generation of orientation augmentations, inaccordance with some examples.

FIG. 21 illustrates mattes, in accordance with some examples.

FIG. 22 illustrates a generative adversarial network (GAN) for trainingconvolutional neural networks (CNNs), in accordance with some examples.

FIG. 23 illustrates an example application of a ML model, in accordancewith some examples.

FIG. 24 illustrates an example application of a ML model, in accordancewith some examples.

FIG. 25 illustrates an example application of a ML model, in accordancewith some examples.

FIG. 26 illustrates a method of generating ground truths for machinelearning models, in accordance with some examples.

FIG. 27 is a diagrammatic representation of a machine in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed herein, in accordance with some examples.

FIG. 28 is a block diagram showing a software architecture within whichexamples may be implemented.

FIG. 29 is a diagrammatic representation of a processing environment, inaccordance with some examples.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative examples of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of various examplesof the inventive subject matter. It will be evident, however, to thoseskilled in the art, that examples of the inventive subject matter may bepracticed without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

In some examples, a ground truth includes ground truth inputs and groundtruth output where the ground truth inputs are matched one-to-one withthe ground truth outputs. The training module for an ML model such asCNN uses as input a ground truth input and then compares the output ofthe CNN with the corresponding ground truth output. In this way, the CNNtraining module can compare the actual output of the CNN with the groundtruth output, which the CNN should have output. The CNN training modulecan then adjust the weights of the CNN so the CNN's output for theground truth input will more closely match the corresponding groundtruth output.

Training ML models such as CNNs is difficult because of the challengesin generating ground truths to use to train the ML models. For example,when the ground truths are composed of images, the problems arecompounded because modifying and rendering images is time consuming andoften beyond the ability of application developers. Additionally, it isdifficult to create ground truths that are both inclusive and diverse.In the following images depicting heads of people or cartoons aredescribed but other images may be used. Inclusive indicates the propertythat different groups of people that may use the trained ML model areincluded in the ground truth. For example, the ground truth shouldinclude people with different color skin, different ages, different faceshapes, different genders, and so forth. Diverse indicates the propertythat the ground truth should have a variability within the inclusiveproperties. For example, the ground truth should include many differentshades of skin color, many different ages, many different expressions onthe faces, many different expressions for the eyes, and so forth.

One example technical problem is how to generate a large enough groundtruth for training the ML model where the ground truth is both diverseand inclusive. The number of pairs of input and output images that arerequired for a ground truth is prohibitively expensive to generate bycapturing actual images or by hand creating each image. In someexamples, the technical problem is addressed by generating a 3D modelfor the ground truth input and a 3D model for the ground truth outputwhere proxy objects are associated with the 3D models. The proxy objectsindicate how to apply a category of augmentations to the 3D models wherea category of augmentations is hair, facial expressions, facialstructure, and so forth. Additionally, the technical problem isaddressed by using fixed augmentations that indicate that augmentationsshould not be applied for a category of the fixed augmentations. Forexample, the ears or skin color may be a property of the 3D model forthe ground truth output so the ears and skin color are indicated asfixed augmentations and not varied for the ground truth output but arevaried for the ground truth input.

Moreover, the example technical problem is addressed by using blendshape augmentations that indicate how to change or modify a 3D model tohave a new characteristic. For example, the 3D models are composed of amess of polygons and a blend shape for smiling indicates how to changethe vertices of the polygons of the 3D model to make a face of the 3Dmodel smile. The blend shapes enable an automating a wide variety ofmodifications to the 3D models.

Additionally, the example technical problem is addressed with matteswhere segments of the 3D models are generated. The segments are used toquickly select portions of the 3D models to modify or applyaugmentations to such as the eyes. In some examples a ML model such as aCNN is trained to process images using the generated ground truth inputand ground truth output. The CNN is then integrated with an application.The ability to generate a ground truth enables application developers todevelop robust image processing applications without spending aprohibitive amount of time developing the ground truth.

Networked Computing Environment

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple instances of a client device102, each of which hosts a number of applications, including a messagingclient 104. Each messaging client 104 is communicatively coupled toother instances of the messaging client 104 and a messaging serversystem 108 via a network 106 (e.g., the Internet).

A messaging client 104 is able to communicate and exchange data withanother messaging client 104 and with the messaging server system 108via the network 106. The data exchanged between messaging client 104,and between a messaging client 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client 104. While certainfunctions of the messaging system 100 are described herein as beingperformed by either a messaging client 104 or by the messaging serversystem 108, the location of certain functionality either within themessaging client 104 or the messaging server system 108 may be a designchoice. For example, it may be technically preferable to initiallydeploy certain technology and functionality within the messaging serversystem 108 but to later migrate this technology and functionality to themessaging client 104 where a client device 102 has sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client 104. Such operations includetransmitting data to, receiving data from, and processing data generatedby the messaging client 104. This data may include message content,client device information, geolocation information, media augmentationand overlays, message content persistence conditions, social networkinformation, and live event information, as examples. Data exchangeswithin the messaging system 100 are invoked and controlled throughfunctions available via user interfaces (UIs) of the messaging client104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, application servers 112. Theapplication servers 112 are communicatively coupled to a database server118, which facilitates access to a database 120 that stores dataassociated with messages processed by the application servers 112.Similarly, a web server 124 is coupled to the application servers 112and provides web-based interfaces to the application servers 112. Tothis end, the web server 124 processes incoming network requests overthe Hypertext Transfer Protocol (HTTP) and several other relatedprotocols.

The Application Program Interface (API) server 110 receives andtransmits message data (e.g., commands and message payloads) between theclient device 102 and the application servers 112. Specifically, theApplication Program Interface (API) server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client 104 in order to invoke functionality of theapplication servers 112. The Application Program Interface (API) server110 exposes various functions supported by the application servers 112,including account registration, login functionality, the sending ofmessages, via the application servers 112, from a particular messagingclient 104 to another messaging client 104, the sending of media files(e.g., images or video) from a messaging client 104 to a messagingserver 114, and for possible access by another messaging client 104, thesettings of a collection of media data (e.g., story), the retrieval of alist of friends of a user of a client device 102, the retrieval of suchcollections, the retrieval of messages and content, the addition anddeletion of entities (e.g., friends) to an entity graph (e.g., a socialgraph), the location of friends within a social graph, and opening anapplication event (e.g., relating to the messaging client 104).

The application servers 112 host a number of server applications andsubsystems, including for example a messaging server 114, an imageprocessing server 116, and a social network server 122. The messagingserver 114 implements a number of message processing technologies andfunctions, particularly related to the aggregation and other processingof content (e.g., textual and multimedia content) included in messagesreceived from multiple instances of the messaging client 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available to themessaging client 104. Other processor and memory intensive processing ofdata may also be performed server-side by the messaging server 114, inview of the hardware requirements for such processing.

The application servers 112 also include an image processing server 116that is dedicated to performing various image processing operations,typically with respect to images or video within the payload of amessage sent from or received at the messaging server 114.

The social network server 122 supports various social networkingfunctions and services and makes these functions and services availableto the messaging server 114. To this end, the social network server 122maintains and accesses an entity graph 306 (as shown in FIG. 3 ) withinthe database 120. Examples of functions and services supported by thesocial network server 122 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following,” and also the identification of other entities andinterests of a particular user.

System Architecture

FIG. 2 is a block diagram illustrating further details regarding themessaging system 100, according to some examples. Specifically, themessaging system 100 is shown to comprise the messaging client 104 andthe application servers 112. The messaging system 100 embodies a numberof subsystems, which are supported on the client-side by the messagingclient 104 and on the server-side by the application servers 112. Thesesubsystems include, for example, an ephemeral timer system 202, acollection management system 204, a modification system 206, a mapsystem 208, a game system 210, and a ground truth generation system 214.

The ephemeral timer system 202 is responsible for enforcing thetemporary or time-limited access to content by the messaging client 104and the messaging server 114. The ephemeral timer system 202incorporates a number of timers that, based on duration and displayparameters associated with a message, or collection of messages (e.g., astory), selectively enable access (e.g., for presentation and display)to messages and associated content via the messaging client 104. Furtherdetails regarding the operation of the ephemeral timer system 202 areprovided below.

The collection management system 204 is responsible for managing sets orcollections of media (e.g., collections of text, image video, and audiodata). A collection of content (e.g., messages, including images, video,text, and audio) may be organized into an “event gallery” or an “eventstory.” Such a collection may be made available for a specified timeperiod, such as the duration of an event to which the content relates.For example, content relating to a music concert may be made availableas a “story” for the duration of that music concert. The collectionmanagement system 204 may also be responsible for publishing an iconthat provides notification of the existence of a particular collectionto the user interface of the messaging client 104.

The collection management system 204 furthermore includes a curationinterface 212 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface212 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certain examples,compensation may be paid to a user for the inclusion of user-generatedcontent into a collection. In such cases, the collection managementsystem 204 operates to automatically make payments to such users for theuse of their content.

The augmentation system 206 provides various functions that enable auser to augment (e.g., annotate or otherwise modify or edit) mediacontent associated with a message. For example, the augmentation system206 provides functions related to the generation and publishing of mediaoverlays for messages processed by the messaging system 100. Theaugmentation system 206 operatively supplies a media overlay oraugmentation (e.g., an image filter) to the messaging client 104 basedon a geolocation of the client device 102. In another example, theaugmentation system 206 operatively supplies a media overlay to themessaging client 104 based on other information, such as social networkinformation of the user of the client device 102. A media overlay mayinclude audio and visual content and visual effects. Examples of audioand visual content include pictures, texts, logos, animations, and soundeffects. An example of a visual effect includes color overlaying. Theaudio and visual content or the visual effects can be applied to a mediacontent item (e.g., a photo) at the client device 102. For example, themedia overlay may include text or image that can be overlaid on top of aphotograph taken by the client device 102. In another example, the mediaoverlay includes an identification of a location overlay (e.g., Venicebeach), a name of a live event, or a name of a merchant overlay (e.g.,Beach Coffee House). In another example, the augmentation system 206uses the geolocation of the client device 102 to identify a mediaoverlay that includes the name of a merchant at the geolocation of theclient device 102. The media overlay may include other indiciaassociated with the merchant. The media overlays may be stored in thedatabase 120 and accessed through the database server 118.

In some examples, the augmentation system 206 provides a user-basedpublication platform that enables users to select a geolocation on a mapand upload content associated with the selected geolocation. The usermay also specify circumstances under which a particular media overlayshould be offered to other users. The augmentation system 206 generatesa media overlay that includes the uploaded content and associates theuploaded content with the selected geolocation.

In other examples, the augmentation system 206 provides a merchant-basedpublication platform that enables merchants to select a particular mediaoverlay associated with a geolocation via a bidding process. Forexample, the augmentation system 206 associates the media overlay of thehighest bidding merchant with a corresponding geolocation for apredefined amount of time.

The map system 208 provides various geographic location functions andsupports the presentation of map-based media content and messages by themessaging client 104. For example, the map system 208 enables thedisplay of user icons or avatars (e.g., stored in profile data 308) on amap to indicate a current or past location of “friends” of a user, aswell as media content (e.g., collections of messages includingphotographs and videos) generated by such friends, within the context ofa map. For example, a message posted by a user to the messaging system100 from a specific geographic location may be displayed within thecontext of a map at that particular location to “friends” of a specificuser on a map interface of the messaging client 104. A user canfurthermore share his or her location and status information (e.g.,using an appropriate status avatar) with other users of the messagingsystem 100 via the messaging client 104, with this location and statusinformation being similarly displayed within the context of a mapinterface of the messaging client 104 to selected users.

The game system 210 provides various gaming functions within the contextof the messaging client 104. The messaging client 104 provides a gameinterface providing a list of available games that can be launched by auser within the context of the messaging client 104, and played withother users of the messaging system 100. The messaging system 100further enables a particular user to invite other users to participatein the play of a specific game, by issuing invitations to such otherusers from the messaging client 104. The messaging client 104 alsosupports both the voice and text messaging (e.g., chats) within thecontext of gameplay, provides a leaderboard for the games, and alsosupports the provision of in-game rewards (e.g., coins and items).

The ground truth generation system 214 provides various functionsrelated to generating ground truths for training machine learningmodels. The ground truth generation system 214 enables a user togenerate a ground truth input 626 and ground truth output 662 fortraining a machine learning model. The ground truth generation system214 provides access to an augmentation database 612 to enable groundtruths to be generated that are diverse and inclusive.

Data Architecture

FIG. 3 is a schematic diagram illustrating data structures 300, whichmay be stored in the database 120 of the messaging server system 108,according to certain examples. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table302. This message data includes, for any particular one message, atleast message sender data, message recipient (or receiver) data, and apayload. Further details regarding information that may be included in amessage and included within the message data stored in the message table302 is described below with reference to FIG. 4 .

An entity table 304 stores entity data, and is linked (e.g.,referentially) to an entity graph 306 and profile data 308. Entities forwhich records are maintained within the entity table 304 may includeindividuals, corporate entities, organizations, objects, places, events,and so forth. Regardless of entity type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 306 stores information regarding relationships andassociations between entities. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example.

The profile data 308 stores multiple types of profile data about aparticular entity. The profile data 308 may be selectively used andpresented to other users of the messaging system 100, based on privacysettings specified by a particular entity. Where the entity is anindividual, the profile data 308 includes, for example, a user name,telephone number, address, settings (e.g., notification and privacysettings), as well as a user-selected avatar representation (orcollection of such avatar representations). A particular user may thenselectively include one or more of these avatar representations withinthe content of messages communicated via the messaging system 100, andon map interfaces displayed by messaging clients 104 to other users. Thecollection of avatar representations may include “status avatars,” whichpresent a graphical representation of a status or activity that the usermay select to communicate at a particular time.

Where the entity is a group, the profile data 308 for the group maysimilarly include one or more avatar representations associated with thegroup, in addition to the group name, members, and various settings(e.g., notifications) for the relevant group.

The database 120 also stores augmentation data, such as overlays orfilters, in an augmentation table 310. The augmentation data isassociated with and applied to videos (for which data is stored in avideo table 314) and images (for which data is stored in an image table316).

Filters, in one example, are overlays that are displayed as overlaid onan image or video during presentation to a recipient user. Filters maybe of various types, including user-selected filters from a set offilters presented to a sending user by the messaging client 104 when thesending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client 104, based ongeolocation information determined by a Global Positioning System (GPS)unit of the client device 102.

Another type of filter is a data filter, which may be selectivelypresented to a sending user by the messaging client 104, based on otherinputs or information gathered by the client device 102 during themessage creation process. Examples of data filters include currenttemperature at a specific location, a current speed at which a sendinguser is traveling, battery life for a client device 102, or the currenttime.

Other augmentation data that may be stored within the image table 316includes augmented reality content items (e.g., corresponding toapplying Lenses or augmented reality experiences). An augmented realitycontent item may be a real-time special effect and sound that may beadded to an image or a video.

As described above, augmentation data includes augmented reality contentitems, overlays, image transformations, AR images, and similar termsrefer to modifications that may be applied to image data (e.g., videosor images). This includes real-time modifications, which modify an imageas it is captured using device sensors (e.g., one or multiple cameras)of a client device 102 and then displayed on a screen of the clientdevice 102 with the modifications. This also includes modifications tostored content, such as video clips in a gallery that may be modified.For example, in a client device 102 with access to multiple augmentedreality content items, a user can use a single video clip with multipleaugmented reality content items to see how the different augmentedreality content items will modify the stored clip. For example, multipleaugmented reality content items that apply different pseudorandommovement models can be applied to the same content by selectingdifferent augmented reality content items for the content. Similarly,real-time video capture may be used with an illustrated modification toshow how video images currently being captured by sensors of a clientdevice 102 would modify the captured data. Such data may simply bedisplayed on the screen and not stored in memory, or the contentcaptured by the device sensors may be recorded and stored in memory withor without the modifications (or both). In some systems, a previewfeature can show how different augmented reality content items will lookwithin different windows in a display at the same time. This can, forexample, enable multiple windows with different pseudorandom animationsto be viewed on a display at the same time.

Data and various systems using augmented reality content items or othersuch transform systems to modify content using this data can thusinvolve detection of objects (e.g., faces, hands, bodies, cats, dogs,surfaces, objects, etc.), tracking of such objects as they leave, enter,and move around the field of view in video frames, and the modificationor transformation of such objects as they are tracked. In variousexamples, different methods for achieving such transformations may beused. Some examples may involve generating a three-dimensional meshmodel of the object or objects, and using transformations and animatedtextures of the model within the video to achieve the transformation. Inother examples, tracking of points on an object may be used to place animage or texture (which may be two dimensional or three dimensional) atthe tracked position. In still further examples, neural network analysisof video frames may be used to place images, models, or textures incontent (e.g., images or frames of video). Augmented reality contentitems thus refer both to the images, models, and textures used to createtransformations in content, as well as to additional modeling andanalysis information needed to achieve such transformations with objectdetection, tracking, and placement.

Real-time video processing can be performed with any kind of video data(e.g., video streams, video files, etc.) saved in a memory of acomputerized system of any kind. For example, a user can load videofiles and save them in a memory of a device, or can generate a videostream using sensors of the device. Additionally, any objects can beprocessed using a computer animation model, such as a human's face andparts of a human body, animals, or non-living things such as chairs,cars, or other objects.

In some examples, when a particular modification is selected along withcontent to be transformed, elements to be transformed are identified bythe computing device, and then detected and tracked if they are presentin the frames of the video. The elements of the object are modifiedaccording to the request for modification, thus transforming the framesof the video stream. Transformation of frames of a video stream can beperformed by different methods for different kinds of transformation.For example, for transformations of frames mostly referring to changingforms of object's elements characteristic points for each element of anobject are calculated (e.g., using an Active Shape Model (ASM) or otherknown methods). Then, a mesh based on the characteristic points isgenerated for each of the at least one element of the object. This meshused in the following stage of tracking the elements of the object inthe video stream. In the process of tracking, the mentioned mesh foreach element is aligned with a position of each element. Then,additional points are generated on the mesh. A first set of first pointsis generated for each element based on a request for modification, and aset of second points is generated for each element based on the set offirst points and the request for modification. Then, the frames of thevideo stream can be transformed by modifying the elements of the objecton the basis of the sets of first and second points and the mesh. Insuch method, a background of the modified object can be changed ordistorted as well by tracking and modifying the background.

In some examples, transformations changing some areas of an object usingits elements can be performed by calculating characteristic points foreach element of an object and generating a mesh based on the calculatedcharacteristic points. Points are generated on the mesh, and thenvarious areas based on the points are generated. The elements of theobject are then tracked by aligning the area for each element with aposition for each of the at least one element, and properties of theareas can be modified based on the request for modification, thustransforming the frames of the video stream. Depending on the specificrequest for modification properties of the mentioned areas can betransformed in different ways. Such modifications may involve changingcolor of areas; removing at least some part of areas from the frames ofthe video stream; including one or more new objects into areas which arebased on a request for modification; and modifying or distorting theelements of an area or object. In various examples, any combination ofsuch modifications or other similar modifications may be used. Forcertain models to be animated, some characteristic points can beselected as control points to be used in determining the entirestate-space of options for the model animation.

In some examples of a computer animation model to transform image datausing face detection, the face is detected on an image with use of aspecific face detection algorithm (e.g., Viola-Jones). Then, an ActiveShape Model (ASM) algorithm is applied to the face region of an image todetect facial feature reference points.

In other examples, other methods and algorithms suitable for facedetection can be used. For example, in some examples, features arelocated using a landmark, which represents a distinguishable pointpresent in a number of the images under consideration. For faciallandmarks, for example, the location of the left eye pupil may be used.If an initial landmark is not identifiable (e.g., if a person has aneyepatch), secondary landmarks may be used. Such landmark identificationprocedures may be used for any such objects. In some examples, a set oflandmarks forms a shape. Shapes can be represented as vectors using thecoordinates of the points in the shape. One shape is aligned to anotherwith a similarity transform (allowing translation, scaling, androtation) that minimizes the average Euclidean distance between shapepoints. The mean shape is the mean of the aligned training shapes.

In some examples, a search for landmarks from the mean shape aligned tothe position and size of the face determined by a global face detectoris started. Such a search then repeats the steps of suggesting atentative shape by adjusting the locations of shape points by templatematching of the image texture around each point and then conforming thetentative shape to a global shape model until convergence occurs. Insome systems, individual template matches are unreliable, and the shapemodel pools the results of the weak template matches to form a strongeroverall classifier. The search is repeated at each level in an imagepyramid, from coarse to fine resolution.

A transformation system can capture an image or video stream on a clientdevice (e.g., the client device 102) and perform complex imagemanipulations locally on the client device 102 while maintaining asuitable user experience, computation time, and power consumption. Thecomplex image manipulations may include size and shape changes, emotiontransfers (e.g., changing a face from a frown to a smile), statetransfers (e.g., aging a subject, reducing apparent age, changinggender), style transfers, graphical element application, and any othersuitable image or video manipulation implemented by a convolutionalneural network that has been configured to execute efficiently on theclient device 102.

In some examples, a computer animation model to transform image data canbe used by a system where a user may capture an image or video stream ofthe user (e.g., a selfie) using a client device 102 having a neuralnetwork operating as part of a messaging client application operating onthe client device 102. The transformation system operating within themessaging client 104 determines the presence of a face within the imageor video stream and provides modification icons associated with acomputer animation model to transform image data, or the computeranimation model can be present as associated with an interface describedherein. The modification icons include changes that may be the basis formodifying the user's face within the image or video stream as part ofthe modification operation. Once a modification icon is selected, thetransform system initiates a process to convert the image of the user toreflect the selected modification icon (e.g., generate a smiling face onthe user). A modified image or video stream may be presented in agraphical user interface displayed on the client device 102 as soon asthe image or video stream is captured, and a specified modification isselected. The transformation system may implement a complexconvolutional neural network on a portion of the image or video streamto generate and apply the selected modification. That is, the user maycapture the image or video stream and be presented with a modifiedresult in real-time or near real-time once a modification icon has beenselected. Further, the modification may be persistent while the videostream is being captured, and the selected modification icon remainstoggled. Machine taught neural networks may be used to enable suchmodifications.

The graphical user interface, presenting the modification performed bythe transform system, may supply the user with additional interactionoptions. Such options may be based on the interface used to initiate thecontent capture and selection of a particular computer animation model(e.g., initiation from a content creator user interface). In variousexamples, a modification may be persistent after an initial selection ofa modification icon. The user may toggle the modification on or off bytapping or otherwise selecting the face being modified by thetransformation system and store it for later viewing or browse to otherareas of the imaging application. Where multiple faces are modified bythe transformation system, the user may toggle the modification on oroff globally by tapping or selecting a single face modified anddisplayed within a graphical user interface. In some examples,individual faces, among a group of multiple faces, may be individuallymodified, or such modifications may be individually toggled by tappingor selecting the individual face or a series of individual facesdisplayed within the graphical user interface.

A story table 312 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a story or a gallery). The creation of a particularcollection may be initiated by a particular user (e.g., each user forwhich a record is maintained in the entity table 304). A user may createa “personal story” in the form of a collection of content that has beencreated and sent/broadcast by that user. To this end, the user interfaceof the messaging client 104 may include an icon that is user-selectableto enable a sending user to add specific content to his or her personalstory.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom varies locations and events. Users whose client devices havelocation services enabled and are at a common location event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client 104, to contribute content to aparticular live story. The live story may be identified to the user bythe messaging client 104, based on his or her location. The end resultis a “live story” told from a community perspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some examples, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

As mentioned above, the video table 314 stores video data that, in oneexample, is associated with messages for which records are maintainedwithin the message table 302. Similarly, the image table 316 storesimage data associated with messages for which message data is stored inthe entity table 304. The entity table 304 may associate variousaugmentations from the augmentation table 310 with various images andvideos stored in the image table 316 and the video table 314. Thedatabase 120 can also store the augmentation database 612 and models 602of FIG. 6 .

Data Communications Architecture

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some examples, generated by a messaging client 104 forcommunication to a further messaging client 104 or the messaging server114. The content of a particular message 400 is used to populate themessage table 302 stored within the database 120, accessible by themessaging server 114. Similarly, the content of a message 400 is storedin memory as “in-transit” or “in-flight” data of the client device 102or the application servers 112. A message 400 is shown to include thefollowing example components:

Message identifier 402 (MSG_ID 402): a unique identifier that identifiesthe message 400. Message text payload 404 (MSG_TEXT 404): text, to begenerated by a user via a user interface of the client device 102, andthat is included in the message 400.

Message image payload 406 (MSG_IMAGE 406): image data, captured by acamera component of a client device 102 or retrieved from a memorycomponent of a client device 102, and that is included in the message400. Image data for a sent or received message 400 may be stored in theimage table 316.

Message video payload 408: video data, captured by a camera component orretrieved from a memory component of the client device 102, and that isincluded in the message 400. Video data for a sent or received message400 may be stored in the video table 314.

Message audio payload 410: audio data, captured by a microphone orretrieved from a memory component of the client device 102, and that isincluded in the message 400.

Message augmentation data 412: augmentation data (e.g., filters,stickers, or other annotations or enhancements) that representsaugmentations to be applied to message image payload 406, message videopayload 408, or message audio payload 410 of the message 400.Augmentation data for a sent or received message 400 may be stored inthe augmentation table 310.

Message duration parameter 414 (MSG_DUR 414): parameter valueindicating, in seconds, the amount of time for which content of themessage (e.g., the message image payload 406, message video payload 408,message audio payload 410) is to be presented or made accessible to auser via the messaging client 104.

Message geolocation parameter 416: geolocation data (e.g., latitudinaland longitudinal coordinates) associated with the content payload of themessage. Multiple message geolocation parameter 416 values may beincluded in the payload, each of these parameter values being associatedwith respect to content items included in the content (e.g., a specificimage into within the message image payload 406, or a specific video inthe message video payload 408).

Message story identifier 418: identifier values identifying one or morecontent collections (e.g., “stories” identified in the story table 312)with which a particular content item in the message image payload 406 ofthe message 400 is associated. For example, multiple images within themessage image payload 406 may each be associated with multiple contentcollections using identifier values.

Message tag 420: each message 400 may be tagged with multiple tags, eachof which is indicative of the subject matter of content included in themessage payload. For example, where a particular image included in themessage image payload 406 depicts an animal (e.g., a lion), a tag valuemay be included within the message tag 420 that is indicative of therelevant animal. Tag values may be generated manually, based on userinput, or may be automatically generated using, for example, imagerecognition.

Message sender identifier 422: an identifier (e.g., a messaging systemidentifier, email address, or device identifier) indicative of a user ofthe Client device 102 on which the message 400 was generated and fromwhich the message 400 was sent.

Message receiver identifier 424: an identifier (e.g., a messaging systemidentifier, email address, or device identifier) indicative of a user ofthe client device 102 to which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 316.Similarly, values within the message video payload 408 may point to datastored within a video table 314, values stored within the messageaugmentations 412 may point to data stored in an augmentation table 310,values stored within the message story identifier 418 may point to datastored in a story table 312, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 304.

Although the described flowcharts can show operations as a sequentialprocess, many of the operations can be performed in parallel orconcurrently. In addition, the order of the operations may bere-arranged. A process is terminated when its operations are completed.A process may correspond to a method, a procedure, an algorithm, etc.The operations of methods may be performed in whole or in part, may beperformed in conjunction with some or all of the operations in othermethods, and may be performed by any number of different systems, suchas the systems described herein, or any portion thereof, such as aprocessor included in any of the systems.

Time-Based Access Limitation Architecture

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message group 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client 104. In one example,an ephemeral message 502 is viewable by a receiving user for up to amaximum of 10 seconds, depending on the amount of time that the sendinguser specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message group 504 (e.g., a collection of messages in apersonal story, or an event story). The ephemeral message group 504 hasan associated group duration parameter 508, a value of which determinesa time duration for which the ephemeral message group 504 is presentedand accessible to users of the messaging system 100. The group durationparameter 508, for example, may be the duration of a music concert,where the ephemeral message group 504 is a collection of contentpertaining to that concert. Alternatively, a user (either the owninguser or a curator user) may specify the value for the group durationparameter 508 when performing the setup and creation of the ephemeralmessage group 504.

Additionally, each ephemeral message 502 within the ephemeral messagegroup 504 has an associated group participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message group504. Accordingly, a particular ephemeral message group 504 may “expire”and become inaccessible within the context of the ephemeral messagegroup 504, prior to the ephemeral message group 504 itself expiring interms of the group duration parameter 508. The group duration parameter508, group participation parameter 510, and message receiver identifier424 each provide input to a group timer 514, which operationallydetermines, firstly, whether a particular ephemeral message 502 of theephemeral message group 504 will be displayed to a particular receivinguser and, if so, for how long. Note that the ephemeral message group 504is also aware of the identity of the particular receiving user as aresult of the message receiver identifier 424.

Accordingly, the group timer 514 operationally controls the overalllifespan of an associated ephemeral message group 504, as well as anindividual ephemeral message 502 included in the ephemeral message group504. In one example, each and every ephemeral message 502 within theephemeral message group 504 remains viewable and accessible for a timeperiod specified by the group duration parameter 508. In a furtherexample, a certain ephemeral message 502 may expire, within the contextof ephemeral message group 504, based on a group participation parameter510. Note that a message duration parameter 506 may still determine theduration of time for which a particular ephemeral message 502 isdisplayed to a receiving user, even within the context of the ephemeralmessage group 504. Accordingly, the message duration parameter 506determines the duration of time that a particular ephemeral message 502is displayed to a receiving user, regardless of whether the receivinguser is viewing that ephemeral message 502 inside or outside the contextof an ephemeral message group 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message group 504based on a determination that it has exceeded an associated groupparticipation parameter 510. For example, when a sending user hasestablished a group participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message group 504 after thespecified twenty-four hours. The ephemeral timer system 202 alsooperates to remove an ephemeral message group 504 when either the groupparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message group 504 has expired, or when theephemeral message group 504 itself has expired in terms of the groupduration parameter 508.

In certain use cases, a creator of a particular ephemeral message group504 may specify an indefinite group duration parameter 508. In thiscase, the expiration of the group participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message group504 will determine when the ephemeral message group 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage group 504, with a new group participation parameter 510,effectively extends the life of an ephemeral message group 504 to equalthe value of the group participation parameter 510.

Responsive to the ephemeral timer system 202 determining that anephemeral message group 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (and, for example, specifically the messaging client 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage group 504 to no longer be displayed within a user interface ofthe messaging client 104. Similarly, when the ephemeral timer system 202determines that the message duration parameter 506 for a particularephemeral message 502 has expired, the ephemeral timer system 202 causesthe messaging client 104 to no longer display an indicium (e.g., an iconor textual identification) associated with the ephemeral message 502.

Generating Ground Truths for Machine Learning

FIG. 6 illustrates generating ground truths for machine learning 600, inaccordance with some examples. The augmentation module 614 processes themodels 602 using the augmentation database 612 to generate augmentations634. The rendering module 660 processes the augmentations 634 togenerate the ground truth input 626 and ground truth output 662. Theground truth input 626 includes augmentations 630 and indexes 632, whichprovides an index 632 to each of the augmentations 630 that have beenapplied to each of the rendered images 628. Similarly, the ground truthoutput 662 includes augmentations 666 and indexes 668, which provides anindex 668 to each of the augmentations 666 that have been applied toeach of the rendered images 664.

The ground truth input 626 and ground truth output 662 are used formachine learning such as training the convolutional neural network (CNN)2206. The models 602 include a base model 604, a simplified base model606, a modified base model 608, and a simplified modified base model610. The models 602 are three-dimensional (3D) meshes of polygons, inaccordance with some examples. The base model 604 and simplified basemodel 606 are used to generate the ground truth input 626. The modifiedbase model 608 and simplified modified base model 610 are used togenerate the ground truth output 662. FIG. 8 is disclosed in conjunctionwith FIG. 6 . FIG. 8 illustrates a base model 806 and a modified basemodel 808, in accordance with some examples. Referring to FIG. 8 , basemodel 806 is the base model 604 and modified base model 808 is themodified base model 608. In some examples, the modified base model 608is derived from the base model 604. For example, the modified base model608 includes turning the base model 604 into a cartoon character byexaggerating the features of the base model 604 to make the modifiedbase model 608 appear like an elfin creature. In other examples, themodified base model 608 is separate from the base model 608. When thebase model 604 or modified base model 608 are discussed, it isunderstood that the discussion applies to several of the models 602. Thebase model 806 and modified base model 808 are composed of a mess ofpolygons with vertices.

The base model 604 and modified base model 608 are sculptured in agraphics application, in some examples. In other examples the base model604 or modified base model 608 are generated from an image or a 3D scanof a head where the image or 3D scan is converted into a 3D modelrepresented by polygons. Faces are the primary focus of the models 602and the augmentation database 612 but other images may be used such asfull human bodies, animals such as horses, objects such as houses,animations such as cartoons, videos, and so forth.

The base model 604 and modified base model 608 have a greater number ofpolygons than the simplified base model 606 and simplified modified basemodel 610, respectively. The reduced number of polygons enables moreaugmentations 634 to be generated and more rendered images 628, 664 tobe generated. Additionally, the fewer polygons simplify the process ofadding some of the augmentations 634. The augmentation module 614 isconfigured to process the base model 604 and modified base model 608 togenerate the simplified base model 606 and simplified modified basemodel 610 using a retopology process.

The augmentation module 614 first processes the base model 604 andsimplified base model 606 to generate augmented base model 636 withaugmentations 638 and indexes 640 and augmented simplified base models642 with augmentations 644 and indexes 646. The augmentation module 614then processes the modified base model 608 and simplified modified basemodel 610 to generate augmented modified base model 648 withaugmentations 650 and indexes 652 and augmented simplified modified basemodel 654 with augmentations 656 with indexes 658. The augmentationmodule 614 uses the same augmentations 638 and augmentations 644 used toprocess the base model 604 and simplified base model 606 to process themodified base model 608 and the simplified based model 610 to generatethe augmentations 650 and augmentations 656. In this way there is aone-to-one correspondence between augmented base models 636 andaugmented modified base models 648 so that they can be used for traininga CNN. Similarly, there is a one-to-one correspondence between theaugmented simplified base models 642 and the augmented simplifiedmodified base models 654.

The normalize module 622 processes the models 602 and the augmentationsfrom the augmentation database 612 so that the augmentations can beapplied to the models 602. The apply module 624 applies theaugmentations to the models 602 to generate the augmentations 634. Thenormalize module 622 and apply module 624 are described in conjunctionwith FIGS. 10-23 .

The augmentation module 614 includes randomization module 616 andnormalize module 622. The randomization module 616 selects theaugmentations 638 and 644 from the augmentation database 612 to apply tothe base model 604. FIG. 7 is disclosed in conjunction with FIG. 6 .FIG. 7 illustrates augmentation database 612, in accordance with someexamples. Augmentation database 612 include item augmentations 702,shader augmentations 704, blendshape augmentations 706 (or blend shapeaugmentations), lighting augmentations 708, and orientationaugmentations 710. When applied by apply module 624, augmentations ofthe augmentation database 612 cause a change to a model of the models602. The augmentations of the augmentation database 612 are furtherdescribed in conjunction with FIGS. 10-23 .

The diversity module 618 ensures that the augmentations 638 and 644selected by the randomization module 616 are diverse for properlytraining the ML models. For example, the diversity module 618 ensuresthat lighting augmentations 708, item augmentations 702, which includesclothing augmentations, and blendshape augmentations 706, which includefacial emotion augmentations, are selected so that the augmentations 638and 644 include a diverse selection of human clothes and facialemotions. The diversity module 618 scans and categorizes theaugmentation database 612 to ensure the diversity. For example, thediversity module 618 determines categories for facial emotions andensures that the selection of augmentations 638 and 644 includes adiverse selection of facial emotions. In some examples, the diversitymodule 618 maintains percentages and then selects the facial emotions ifthe percentage falls below a predetermined threshold. For example, ifthe augmentations 638 and 644 include only 10 percentage of facialaugmentations that transform the base model 604 into a smile, and apredetermined percentage is to have 15 percentage of augmentations 638and 644 that transform the base model 604 into a smile, then thediversity module 618 ensures that more augmentations 638 and 644 areselected that transform the base model 604 into a smile. The diversitypercentage for smile can be determined based on counting all theaugmentations that transform the base model 604 into a smile divided bythe total number of augmentations that transform the base model 604 intoa facial expression.

The inclusivity module 620 ensures that the augmentations 638 and 644include augmentations from the augmentation database 612 to avoidimplicit biases. For example, the inclusivity module 620 ensures thataugmentations 638 and 644 include different skin colors, differentfacial structures, different genders, and so forth. The inclusivitymodule 620 maintains an inclusivity percentage for each of the skincolors, the facial structures, and the genders selected for theaugmentations 638 and 644. If a percentage falls below a predeterminedthreshold, then the inclusivity module 620 selects the augmentations 638and 644 to ensure that the inclusivity percentage is raised above thepredetermined threshold. The predetermined thresholds for theinclusivity module 620 are predetermined values.

Fixed augmentations 621 indicates augmentations 638 and 644 that are tobe excluded from augmentations 650, 656. For example, referring to FIG.8 , the ears of modified base model 808 are fixed and are part of thetransformation that the ML model will perform. Augmentations that modifythe ears of the base model 604 and simplified base model 606 are notincluded for augmented modified base model 648 and augmented simplifiedmodified base model 654. Other augmentation may be included in fixedaugmentations 621. For example, if the modified base model 608 has afixed skin color like blue, then augmentations 638 and 644 that modifythe skin color are excluded from augmentations 650, 656. In otherexamples, fixed augmentations 621 includes lighting, shading, facialexpressions, an ear shape, a clothing item, and so forth. In someexample, a user that is generating the ground truth input 626 and theground truth output 662 indicates the fixed augmentations 621 using auser interface.

The rendering module 660 processes the augmentations 634 and generatesthe rendered images 628 and 664. Rendering high-quality images iscomputationally intensive. Depending on the complexity of the models 602and the augmentations 638, 644, 650, and 656 applied to the models 602,rendering a 1024×1024 image on a machine with a modern graphicsprocessing unit (GPU) takes between two and ten minutes. To speed up therendering, the rendering module 660 is configured to render the imageson cloud machines such as web server 124 or application servers 112.Each cloud machine runs an independent rendering process. Each renderingprocess takes one of the augmentations 634 with all required resources,such as models 602 and augmentations from the augmentation database 612and compiles a set of rendered images 628, 664. Each rendered image 628,664 represents a unique combination of an augmented model, such asaugmented base model 636, together with lighting augmentations 708 andorientation augmentations 710. In some examples, the rendering module660 generates files for another applications such as Blender® running onthe web servers.

FIG. 9 is disclosed in conjunction with FIG. 6 . FIG. 9 illustratesdifferent faces 900 in accordance with some examples. The faces 902,904, 906 illustrate the diversity in faces in terms of structure, skincolor, hair, clothing, backgrounds, facial expressions, head tilt,lighting, and so forth. The augmentation database 612 includesaugmentations so that the ground truth input 626 includes a diverse andinclusive set of rendered images 628. There are many different degreesof freedom that need to be varied to have a diverse and inclusive groupof augmentations 634 to train a ML model. For heads of people orcartoons the augmentation database 612 includes augmentations forvarying skin color, skin texture, face geometry, gender, accessoriessuch as glasses, clothes, eye expressions, eye color, gaze direction,mouth expressions, brow expressions, and object orientation.

FIG. 10 illustrates an example of rendered images and segmentation maps1000, in accordance with some examples. The rendered images 1006, 1008,and 1010 are based on the base model 806 or modified base model 808 ofFIG. 8 . The rendered images 1006, 1008, and 1010 include differentaugmentations for the hair, head tilt, clothes, skin color, eyesexpression, and backgrounds. The segmentation maps 1012, 1014, and 1016correspond to the rendered images 1006, 1008, and 1010 and are used tofacilitate applying augmentations to the base model 808 by the applymodule 624. Additionally, the segmentation maps 1012, 1014, and 1016illustrate that different hair augmentations are applied, different headtilts are applied, different eyes expressions are applied, differentclothes augmentations are applied, and that different mouth expressionsare applied. Although not illustrated, the same augmentations that areapplied to rendered images 1006, 1008, and 1010 are applied to both thebase model 806 and the modified base model 608 so that for each of therendered images 1006, 1008, 1010 there is a corresponding rendered imagewith the same augmentations derived from the base model 604 or modifiedbase model 608. But the base model 806 or modified base model 608 mayhave an additional augmentation that modifies the ears that is a fixedaugmentation 621 and, thus, not applied to the modified base model 608or base model 806 by the apply module 624.

FIG. 11-13 are described in conjunction with one another. FIG. 11illustrates clothing augmentations. Illustrated in FIG. 11 is threeclothing augmentations 1102, 1104, and 1106. The clothing augmentations1102, 1104, and 1106 are item augmentations 702 from the augmentationdatabase 612. The clothing augmentations 1102, 1104, 1106 are 3D objects714 with their own texture, shape, and colors. Item augmentations 702are augmentations that are separate objects that are added to the basemodel 604 or modified base model 608. The item augmentations 702 includehair, clothing, accessories, glasses, and so forth.

The base model 604 and modified base model 608 have proxy objects 716for each type of 3D object 714. FIG. 12 illustrates a proxy object 1206,in accordance with some examples. 3D model 1202 is a modified base model608 such as base model 808 with a proxy object 1206. 3D model 1204 isthe modified base model 608 with the clothing augmentation 1102 appliedto the 3D model 1202 in accordance with the proxy object 1206 togenerate clothing augmentation 1208. The proxy object 1206 is a mess ofpolygons that indicates how to apply the clothing augmentation Thenormalize module 622 resizes the clothing augmentation 1102 to fit tothe proxy object 1206 and the apply module 624 merges the resizedclothing augmentation 1102 with the proxy object 1206 to generate the 3Dmodel 1204 with the clothing augmentation 1102. The color of theclothing augmentation 1102 may be varied. Referring to FIG. 6 , the 3Dmodel 1204 is an augmented modified base model 648 such as base model808 with an augmentation 650 having an index 652 where the augmentation650 is the clothing augmentation 1102. The augmentation module 614generates a corresponding augmented base model 636 such as base model806 with augmentation 638 having an index 640 where augmentation 638 isthe same clothing augmentation 1102 applied to the base model 806.

FIG. 13 illustrates a proxy object 1306, in accordance with someexamples. 3D model 1302 is a modified base model 608 such as base model808 with a proxy object 1306. 3D model 1304 is the modified base model608 with a hair augmentation (not illustrated) applied to the 3D model1302 in accordance with the proxy object 1306 to generate hairaugmentation 1308. The proxy object 1306 is a mess of polygons thatindicates how to apply the hair augmentation The normalize module 622resizes the hair augmentation to fit to the proxy object 1306 and theapply module 624 merges the resized hair augmentation with the proxyobject 1306 to generate the 3D model 1304 with the hair augmentation1308. The color of the hair augmentation may be varied. Referring toFIG. 6 , the 3D model 1304 is an augmented modified base model 648 suchas base model 808 with an augmentation 650 having an index 652 where theaugmentation 650 is a hair augmentation. The augmentation module 614generates a corresponding augmented base model 636 such as base model806 with augmentation 638 having an index 640 where augmentation 638 isthe same hair augmentation applied to the base model 806.

FIG. 14 illustrates skin textures 1402, 1404, 1406, and 1406, inaccordance with some examples. The skin textures 1402, 1404, 1406, and1408 are stored in the augmentation database 612 as shader augmentations704 that are skin textures 718. The skin textures 1402, 1404, 1406, and1406 are applied to the base model 604 and modified base model 608.Referring to FIG. 10 , rendered image 1006 is augmented with skintexture 1404, rendered image 1008 is augmented with skin texture 1408,and rendered image 1010 is augmented with skin texture 1402. The skintextures 1402, 1404, 1406, and 1406 cover different textures used on thebase model 604 and modified base model 608 and may be used to coverother augmentations from augmentation database 612 such as itemaugmentations 702. The skin textures 1402, 1404, 1406, and 1408 areobjects represented with polygons and colors. The normalize module 622resizes the skin textures 1402, 1404, 1406, and 1408 in accordance withthe base model 604 or modified base model 608. For example, normalizemodule 622 resized skin texture 1408 to fit on the base model 808. Theapply module 624 then applies the resized skin texture 1408 to the basemodel 808. Other augmentations are then added and then rendered togenerate rendered image 1008.

FIG. 15 illustrates eye textures 1502, 1504, 1506, and 1506, inaccordance with some examples. The eye textures 1502, 1504, 1506, and1508 are stored in the augmentation database 612 as shader augmentations704 that are eye textures 720. The eye textures 1502, 1504, 1506, and1506 are applied to the base model 604 and modified base model 608.Referring to FIG. 10 , rendered image 1006 is augmented with eye texture1502, rendered image 1008 is augmented with eye texture 1504, andrendered image 1010 is augmented with eye texture 1506. The eye textures1502, 1504, 1506, and 1506 cover different textures used on the basemodel 604 and modified base model 608 and may be used to cover otheraugmentations from augmentation database 612 such as item augmentations702. The eye textures 1502, 1504, 1506, and 1508 are objects representedwith polygons and colors. The normalize module 622 resizes the eyetextures 1502, 1504, 1506, and 1508 in accordance with the base model604 or modified base model 608. For example, normalize module 622resized eye texture 1502 to fit on the base model 808. The apply module624 then applies the resized eye texture 1502 to the base model 808.Other augmentations are then added and then rendered to generaterendered image 1006.

FIG. 16 illustrates applying blendshape augmentations to a 3D basemodel, in accordance with some examples. Blendshape augmentations 706(or morph augmentations) are indications of how the models 602 should bemorphed or changed. For example, the blendshape augmentation 706indicates changes to the locations of the vertices of the polygons ofthe models 602. The blendshape augmentations 706 include augmentationsfor changing the models 602 for different groups of people; body types;genders; mouth expressions including sad, smiling, laughing, kissing,wide smile, surprised, and extremely wide-opened mouth; eyes includinggaze direction, eyes expression, and eyes blinking; and, brows includingraised brow and lowered brow.

3D model 1602, which may be the same as base model 808 is morphed orchanged by blendshape 1 1610, blendshape 2 1612, and blendshape 3 1614.Normalize module 622 adjusts blendshape 1 1610, blendshape 2 1612, andblendshape 3 1614, and then the apply module 624 applies the blendshapesto the 3D model 1602. Each of the new 3D models 1604, 1606, and 1608 areaugmented modified base models 648 with augmentations 650 as blendshape1 1610, blendshape 2 1612, and blendshape 3 1614, respectively. Theaugmentation module 614 generates augmented base model 636 withaugmentations 638 corresponding to augmentations 650. Blendshape 1 1610takes the 3D model 1602 and adjusts the 3D model for different facialfeatures that may be attributed to a particular group of people forinclusivity considerations. Blendshape 2 1612 takes the 3D model 1602and adjust the facial feature type to a broader face. Blendshape 3 1614takes the 3D model 1602 and adjusts the facial features for a maleversion of the 3D model 1602. As illustrated the new 3D models 1604,1606, and 1608 include additional augmentations such as hairaugmentations and clothing augmentations.

FIG. 17 illustrates High Dynamic Range Imagery (HDRI) lighting 1700, inaccordance with some examples. The augmentation database includeslighting augmentations 708 that are applied by the rendering module 660in generating the rendered images 628 and 664.

ML models such as CNN 2206 are sensitive to the lighting used for therendered images 628, 664. ML models that are trained with the groundtruth input 626 and the ground truth output 662 where the lighting usedto render the rendered images 628, 664 is only even bright light oftenwill fail to generate satisfactory output images when the input image isdark or shady. The diversity module 618 ensures that a diversity oflighting augmentations 708 is used. The lighting augmentations 708should include for heads bright lighting, dark lighting, and shadylighting. There two types of lighting augmentations 708. HDRI lighting1700, which is a panoramic photo and covers all angles from a singlepoint. Rendering module 660 uses the HDRI lighting 1700 to render therendered images 628, 664. HDRI lighting 1700 enables the background of arendered image to be augmented without re-rendering the rendered image.FIG. 18 illustrates HDRI lighting 1800, in accordance with someexamples. HDRI lighting 1800 is another example of HDRI lighting.

FIG. 19 illustrates rendered images with different lighting 1900, inaccordance with some examples. Lighting augmentations 708 include usinglighting sources such as lamps and configuring their location andbrightness in the scene with the augmented base model 636 and augmentedmodified base model 648. The rendering module 660 rendered the renderedimages 1902, 1904, 1906, and 1908 with different lighting augmentations708. In some examples, lighting augmentations 708 are used that are morelikely to be used in application after the ML model has been trained.For example, one lighting augmentation 708 is a single light source fora self-portrait taken on mobile device, which may be a common inputimage for the ML model after it has been trained with the ground truthinput 626 and ground truth output 662.

FIG. 20 illustrates the generation of orientation augmentations 2000, inaccordance with some examples. Illustrated in FIG. 20 is the 3D model2002, the z-axis 2008, 2014, the y-axis 2010, 2016, the x-axis 2012,2018, an anchor arrow 2004, and a vector (V) 2020 that is visualizationline for the 3D model 2002. Referring to FIGS. 6 and 7 , to train MLmodels the ground truth input 626 and ground truth output 662 includethe base model 604 and modified base model 608 rendered by the renderingmodule 660 with different orientation augmentations 710.

The goal of the orientation augmentations 710 is to provide orientationsin the ground truth input 626 and the ground truth output 662 that willbe seen in an input to the ML model in production. The augmentationmodule 614 is configured to randomly select orientations augmentations700 for the base model 608 over the y-axis 2010, 2016, which moves thebase model 608 up and down and is termed pitch, and the x-axis 2012,2018, which moves the base model 608 left and right and is termed yaw.In some examples, the z-axis 2008, 2014 is not changed when the 3D model2002 is a crop of a user's head. A change in the z-axis 2008, 2014 wouldrotate the base model 608 and is termed roll. The input to the ML modelin production crops a user's head, which forces the z-axis 2008, 2014angle to be zero.

The augmentation module 614 adds an anchor arrow 2004 that is an objectto the base model 608 that is located behind the head and bound to the3D model 2002 so that the 3D model 2002 “looks” along vector V 2020going from the anchor arrow 2004 to the point on the back of the head.The anchor arrow 2004 can be visualized as a ball and vector V 2020 is aline. The augmentation module 614 uses a circle in a plane formed by they-axis 2010, 2016 and the x-axis 2012, 2018 and makes a full turn over acircle in the plane to select orientation augmentations 710 such asselecting 1000 orientation augmentations 710 based dividing the circleinto 1000 discrete steps. In some examples, orientation augmentations710 are not selected for orientations of the 3D model 2002 with thevector V 2020 would be looking backward or away.

FIG. 21 illustrates mattes, in accordance with some examples. Theaugmentation module 614 is configured to generate mattes 2106 and 2108from a base model 604. 3D models 2102 and 2104 are rendered images froma modified base model 608 such as base model 808 where augmentationsfrom the augmentation database 612 have been added and then rendered bythe rendering module 660. The mattes 2106 and 2108 enable theaugmentation module 614 to select portions of the augmented modifiedbase model 648 or rendered images 664 to add or modify an augmentation.The mattes 2106 and 2108 are associated with indexes or labels thatindicate the different portions of the mattes 2106 and 2108. Forexample, the eye area has a label of eyes and the augmentation module614 can use the mattes 2106 and 2108 to select the eye area of 3D models2102 and 2104 and change the eye area such as by changing the color ofthe eyes. In some examples the mattes are Cryptomattes®. The indexes orlabels of the mattes 2106 and 2108 are the same as the indexes 640, 646,652, 658, 632, and 668. Additionally, the augmentation database 612includes indexes associated with augmentations stored in theaugmentation database 612.

FIG. 22 illustrates a generative adversarial network (GAN) 2200 fortraining convolutional neural networks (CNNs), in accordance with someexamples. FIG. 22 illustrates how the ground truth input 626 and groundtruth output 662 can be used to trains a ML model such as CNN 2206. TheCNN 2206 takes ground truth input image 2202 and generates or processesground truth input image 2202 to generate output image 2208. Groundtruth input image 2202 is the same or similar as ground rendered image628 from ground truth input 626 and ground truth output image 2216 isthe same or similar as rendered image 664 from ground truth output 662.

The CNN 2206, loss network 2212, and discriminator network 2223 areconvolutional neural networks, in accordance with some examples. Eachhas multiple convolutional layers, pooling layers, and fully connectedlayers, in accordance with some examples. One or more of the networksmay have up sampling and down sampling. One or more of the networks mayhave layers that are connected to the next layer in the network and anadditional layer closer to the output layer. The fully connected layersuse rectified linear unit (ReLU), in accordance with some examples.

Weight adjustment module 2224 is configured to adjust the weights 2204of the CNN 2206 based on the perceptual losses 2218 and adversariallosses from the discriminator network 2223. Weight adjustment module2224 adjusts the weights 2204 based on using a stochastic gradientdescent method to determine weights 2204 that minimize or lessen theweighted sum of the loss functions. Weight adjustment module 2224additionally trains discriminator network 2223 by changing the weights2225 as described herein.

The perceptual loss 2218 is determined with the aid of a number oftrained neural networks (NN) 2220. The loss network 2212 is trainedbased on images representing high-level features that are grouped intosets of high-level features, in accordance with some examples. Thehigh-level features may include coloring information and lightinginformation. Each of the NNs 2220 may be trained for one or morehigh-level features by adjusting the weights 2214. The trained NNs 2220determine high-level features for both the output image 2208 and theground truth output image 2216. The perceptual loss 2218 is based ondetermining a high-level feature loss of the output image 2208 from theground truth output image 2216. The perceptual loss is then determinedby weight adjustment module 2224 based on regression analysis, inaccordance with some examples. The weight adjustment module 2224 usesEquation (1) to determine the perceptual loss (loss_(per)), inaccordance with some examples.

Equation (1): Loss_(per)=E[Σ_(i=1) ^(n) w_(i)l_(feat)(y_(i), ŷ_(i))],where y_(i) is the ground truth output image 2216, ŷ_(i) is the outputimage 2208, E is the expected value of the summation, n is the number ofground truth pairs, l_(feat) is the feature reconstruction loss betweeny_(i) and ŷ_(i) for the features in accordance with the trained NNs2220, and w_(i) is a weight assigned to the feature i.

The loss of the CNN 2206 is determined by adjust weights module 2224using Equation (2). Equation (2): G_(loss)=E[log (1−D(G(x))], whereG_(loss) is the loss for image transformation network, E is the expectedvalue, and D is the determination of the discriminator network 2223.

The discriminator network 2223 is trained to take as input the groundtruth input image 2202 and an output image 2208 and output a valuebetween 0 and 1 to indicate the likelihood that the output image 2208 isthe ground truth output image 2216. The loss of the discriminatornetwork 2223 is determined by weight adjustment module 2224 inaccordance with Equation (3).

Equation (3): D_(loss)=−E[log(D(x_(real)))+ log (1−D(G(x)))], whereD_(loss) is the loss for the discriminator network 2223, E is theexpected value, x is the ground truth input image 2202, and x_(real) isthe ground truth output image 2216, D(x_(real)) is the prediction suchas a value from 0 to 1 for whether x_(real) is the ground truth outputimage 2216, and D(G(x)) is the prediction such as a value from 0 to 1for whether G(x), which is output image 2208, is the ground truth outputimage 2216.

Weight adjustment module 2224 determines the loss function for the CNN2206 in accordance with Equation (4). Equation (4):Loss=Loss_(per)+α*G_(loss), where loss is the loss used to train the CNN2206, Loss_(per) is determined in accordance with Equation (1), G_(loss)is determined in accordance with Equation (2), and a is a constant lessthan 1.

Weight adjustment module 2224 trains CNN 2206 and discriminator network2223 in conjunction with one another. As the discriminator network 2223becomes better at determining whether the output image 2208 is theground truth output image 2216 or not, the CNN 2206 is trained to makethe output image 2208 more like the ground truth output image 2216. Inthis way the two networks help each other train because as thediscriminator network 2223 improves in distinguishing the output image2208 and the ground truth output image 2216, the CNN 2206 improves ingenerating the output image 2208 to being closer to the ground truthoutput image 2216. Because the system for generating ground truths formachine learning 600 can generate an arbitrarily large set of trainingpairs under many different lighting scenarios and different 3D models,the CNN 2206 can be trained to process or transform the lighting undermany different lighting scenarios and many different 3D models.

FIG. 23 illustrates an example application of a ML model, in accordancewith some examples. FIG. 23 illustrates a mobile device 2302, which maybe a client device 102, in accordance with some examples. The mobiledevice 2302 may include a camera 2306 and screen 2304. As illustrated,an input image 2310 is processed to generate output image 2312. Theoutput image 2312 has areas that are lightened 2314 and areas that aredarkened 2316 relative to the input image 2310. A user of the mobiledevice 2302 has selected for the input image 2310 to be neutralized2318. The user may select to save 2320 the output image 2312 or send2322 the output image 2312 such as through the messaging system 100 asan ephemeral message. The user may select edit and enhance 2308 tochange the output image 2312 or add augmentations to the output image2312. In some examples edit and enhance 2308 offers the user relightingoptions. In some examples, neutralize 2318 is offered as an option froma menu presented when edit and enhance 2308 is selected. The neutralizefunction is provided by a CNN 2206 trained with ground truth input 626and ground truth output 662 where the neutralized light for the modifiedbase model 608 is a fixed augmentation 621 so that different lightingaugmentations 708 for the ground truth input 626 are changed toneutralized light.

FIG. 24 illustrates an example application of a ML model, in accordancewith some examples. As illustrated, an input image 2402 is processed togenerate output image 2404. The input image 2402 has object 1 2408 andobject 2 2410 that were added to an image using the edit and enhance2308 features. The object 1 2408 and object 2 2410 have differentlighting than the image of the person in input image 2402. The relight2406 operation adjusts the lighting of object 1 2408 and object 2 2410to generate modified object 1 2412 and modified object 2 2423. Therelight function is provided by a CNN 2206 trained with ground truthinput 626 and ground truth output 662 where item augmentations 702includes object 1 2408 and object 2 2410 as well as other augmentationsor objects that can be added to the input image 2402. The lighting ofthe item augmentations 702 is a fixed augmentation 621 so the lightingremains constant for the ground truth input 626 and lighting is adjustedor normalized for the ground truth output 662.

FIG. 25 illustrates an example application of a ML model, in accordancewith some examples. As illustrated, an input image 2502 is processed togenerate output image 2504. The input image 2502 is an image of a faceof a person. The output image 2504 is an image of the face of the personturned into a cartoon image. The cartoon 2506 operation turns the faceof the person into a cartoon. The cartoon function is provided by a CNN2206 trained with ground truth input 626 and ground truth output 662where the base model 604 is of a person and the modified base model 608is of the person turned into a cartoon image. The augmentation module614 and rendering module 660 select the augmentations from theaugmentation database 612 to provide diverse and inclusive augmentationsso that the CNN 2206 is trained to accommodate all types of people.

FIG. 26 illustrates a method 2600 of generating ground truths formachine learning models, in accordance with some examples. The methodbegins at operation 2602 with processing a first 3D base model togenerate a plurality of first rendered images. For example, processing afirst 3D base model to generate a plurality of first images, each firstimage of the plurality of first images depicting the first 3D base modelmodified by first augmentations of a plurality of augmentations.Referring to FIG. 16 , the augmentation module 614 and the renderingmodule 660 generated 3D models 1604, 1606, and 1608 from base model 808where augmentation module 614 applied augmentations from theaugmentation database 612 such as hair and clothes to the 3D model 1602.

The method 2600 continues at operation 2604 with determining for asecond 3D base model incompatible augmentations of the plurality ofaugmentations. For example, determining, for a second 3D base model,incompatible augmentations of the first augmentations, the incompatibleaugmentations indicating modifications to fixed features of the second3D base model. Referring to FIG. 8 , the base model 808 has fixedfeatures of the ears so any augmentations that change the ears are notapplied to the base model 808.

The method 2600 continues at operation 2606 with processing the second3D base model to generate a plurality of second rendered images, eachsecond rendered image comprising the second 3D base model modified bysecond augmentations. For example, processing the second 3D base modelto generate a second image for each of the plurality of first images,each second image depicting the second 3D base model modified by secondaugmentations, the second augmentations corresponding to the firstaugmentations of a corresponding first image, wherein the secondaugmentations comprises augmentations of the first augmentations thatare not incompatible augmentations. Referring to FIG. 10 , theaugmentation module 614 and the rendering module 660 generated renderedimages 1006, 1008, and 1010 from base model 808 where augmentationmodule 614 applied augmentations from the augmentation database 612 suchas hair and clothes to the base model 808 but did not applyaugmentations that would change the ears.

One or more of the operations of method 2600 may be optional. Method2600 may include one or more additional operations. The operations ofmethod 2600 may be performed in a different order.

Machine Architecture

FIG. 27 is a diagrammatic representation of the machine 2700 withinwhich instructions 2708 (e.g., software, a program, an application, anapplet, an app, or other executable code) for causing the machine 2700to perform any one or more of the methodologies discussed herein may beexecuted. For example, the instructions 2708 may cause the machine 2700to execute any one or more of the methods described herein. Theinstructions 2708 transform the general, non-programmed machine 2700into a particular machine 2700 programmed to carry out the described andillustrated functions in the manner described. The machine 2700 mayoperate as a standalone device or may be coupled (e.g., networked) toother machines. In a networked deployment, the machine 2700 may operatein the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine 2700 maycomprise, but not be limited to, a server computer, a client computer, apersonal computer (PC), a tablet computer, a laptop computer, a netbook,a set-top box (STB), a personal digital assistant (PDA), anentertainment media system, a cellular telephone, a smartphone, a mobiledevice, a wearable device (e.g., a smartwatch), a smart home device(e.g., a smart appliance), other smart devices, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing the instructions 2708, sequentially or otherwise,that specify actions to be taken by the machine 2700. Further, whileonly a single machine 2700 is illustrated, the term “machine” shall alsobe taken to include a collection of machines that individually orjointly execute the instructions 2708 to perform any one or more of themethodologies discussed herein. The machine 2700, for example, maycomprise the client device 102 or any one of a number of server devicesforming part of the messaging server system 108. In some examples, themachine 2700 may also comprise both client and server systems, withcertain operations of a particular method or algorithm being performedon the server-side and with certain operations of the particular methodor algorithm being performed on the client-side.

The machine 2700 may include processors 2702, memory 2704, andinput/output I/O components 2738, which may be configured to communicatewith each other via a bus 2740. The processors 2702 may be termedcomputer processors, in accordance with some examples. In an example,the processors 2702 (e.g., a Central Processing Unit (CPU), a ReducedInstruction Set Computing (RISC) Processor, a Complex Instruction SetComputing (CISC) Processor, a Graphics Processing Unit (GPU), a DigitalSignal Processor (DSP), an Application Specific Integrated Circuit(ASIC), a Radio-Frequency Integrated Circuit (RFIC), another processor,or any suitable combination thereof) may include, for example, aprocessor 2706 and a processor 2702 that execute the instructions 2708.The term “processor” is intended to include multi-core processors thatmay comprise two or more independent processors (sometimes referred toas “cores”) that may execute instructions contemporaneously. AlthoughFIG. 27 shows multiple processors 2702, the machine 2700 may include asingle processor with a single-core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiples cores, or any combinationthereof.

The memory 2704 includes a main memory 2712, a static memory 2714, and astorage unit 2716, both accessible to the processors 2702 via the bus2740. The main memory 2704, the static memory 2714, and storage unit2716 store the instructions 2708 embodying any one or more of themethodologies or functions described herein. The instructions 2708 mayalso reside, completely or partially, within the main memory 2712,within the static memory 2714, within machine-readable medium 2718within the storage unit 2716, within at least one of the processors 2702(e.g., within the Processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 2700.

The I/O components 2738 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 2738 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 2738 mayinclude many other components that are not shown in FIG. 27 . In variousexamples, the I/O components 2738 may include user output components2724 and user input components 2726. The user output components 2724 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light-emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The userinput components 2726 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and force of touches or touch gestures, or other tactile inputcomponents), audio input components (e.g., a microphone), and the like.

In further examples, the I/O components 2738 may include biometriccomponents 2728, motion components 2730, environmental components 2732,or position components 2734, among a wide array of other components. Forexample, the biometric components 2728 include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye-tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 2730 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope).

The environmental components 2732 include, for example, one or cameras(with still image/photograph and video capabilities), illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetect ion sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment.

With respect to cameras, the client device 102 may have a camera systemcomprising, for example, front cameras on a front surface of the clientdevice 102 and rear cameras on a rear surface of the client device 102.The front cameras may, for example, be used to capture still images andvideo of a user of the client device 102 (e.g., “selfies”), which maythen be augmented with augmentation data (e.g., filters) describedabove. The rear cameras may, for example, be used to capture stillimages and videos in a more traditional camera mode, with these imagessimilarly being augmented with augmentation data. In addition to frontand rear cameras, the client device 102 may also include a 360° camerafor capturing 360° photographs and videos.

Further, the camera system of a client device 102 may include dual rearcameras (e.g., a primary camera as well as a depth-sensing camera), oreven triple, quad or penta rear camera configurations on the front andrear sides of the client device 102. These multiple cameras systems mayinclude a wide camera, an ultra-wide camera, a telephoto camera, a macrocamera and a depth sensor, for example.

The position components 2734 include location sensor components (e.g., aGPS receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 2738 further include communication components 2736operable to couple the machine 2700 to a network 2720 or devices 2722via respective coupling or connections. For example, the communicationcomponents 2736 may include a network interface Component or anothersuitable device to interface with the network 2720. In further examples,the communication components 2736 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 2722 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a USB).

Moreover, the communication components 2736 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 2736 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components2736, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., main memory 2712, static memory 2714, andmemory of the processors 2702) and storage unit 2716 may store one ormore sets of instructions and data structures (e.g., software) embodyingor used by any one or more of the methodologies or functions describedherein. These instructions (e.g., the instructions 2708), when executedby processors 2702, cause various operations to implement the disclosedexamples.

The instructions 2708 may be transmitted or received over the network2720, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components2736) and using any one of several well-known transfer protocols (e.g.,hypertext transfer protocol (HTTP)). Similarly, the instructions 2708may be transmitted or received using a transmission medium via acoupling (e.g., a peer-to-peer coupling) to the devices 2722.

Software Architecture

FIG. 28 is a block diagram 2800 illustrating a software architecture2804, which can be installed on any one or more of the devices describedherein. The software architecture 2804 is supported by hardware such asa machine 2802 that includes processors 2820, memory 2826, and I/Ocomponents 2838. In this example, the software architecture 2804 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 2804 includes layerssuch as an operating system 2812, libraries 2810, frameworks 2808, andapplications 2806. Operationally, the applications 2806 invoke API calls2850 through the software stack and receive messages 2852 in response tothe API calls 2850.

The operating system 2812 manages hardware resources and provides commonservices. The operating system 2812 includes, for example, a kernel2814, services 2816, and drivers 2822. The kernel 2814 acts as anabstraction layer between the hardware and the other software layers.For example, the kernel 2814 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 2816 canprovide other common services for the other software layers. The drivers2822 are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 2822 can include display drivers,camera drivers, BLUETOOTH® or BLUETOOTH® Low Energy drivers, flashmemory drivers, serial communication drivers (e.g., USB drivers), WI-FI®drivers, audio drivers, power management drivers, and so forth.

The libraries 2810 provide a common low-level infrastructure used by theapplications 2806. The libraries 2810 can include system libraries 2818(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 2810 can include APIlibraries 2824 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 2810 can also include a widevariety of other libraries 2828 to provide many other APIs to theapplications 2806.

The frameworks 2808 provide a common high-level infrastructure that isused by the applications 2806. For example, the frameworks 2808 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 2808 canprovide a broad spectrum of other APIs that can be used by theapplications 2806, some of which may be specific to a particularoperating system or platform.

In an example, the applications 2806 may include a home application2836, a contacts application 2830, a browser application 2832, a bookreader application 2834, a ground truths 2841 generation application, alocation application 2842, a media application 2844, a messagingapplication 2846, a game application 2848, and a broad assortment ofother applications such as a third-party application 2840. The groundtruths 2841 generation application may perform the operations asdisclosed in conjunction with FIG. 6 and herein. The applications 2806are programs that execute functions defined in the programs. Variousprogramming languages can be employed to create one or more of theapplications 2806, structured in a variety of manners, such asobject-oriented programming languages (e.g., Objective-C, Java, or C++)or procedural programming languages (e.g., C or assembly language). In aspecific example, the third-party application 2840 (e.g., an applicationdeveloped using the ANDROID™ or IOS™ software development kit (SDK) byan entity other than the vendor of the particular platform) may bemobile software running on a mobile operating system such as IOS™,ANDROID™, WINDOWS® Phone, or another mobile operating system. In thisexample, the third-party application 2840 can invoke the API calls 2850provided by the operating system 2812 to facilitate functionalitydescribed herein.

Processing Components

Turning now to FIG. 29 , there is shown a diagrammatic representation ofa processing environment 2900, which includes a processor 2902, aprocessor 2906, and a processor 2908 (e.g., a GPU, CPU or combinationthereof). The processor 2902 is shown to be coupled to a power source2904, and to include (either permanently configured or temporarilyinstantiated) modules, namely an augmentation component 2910, arendering component 2912, and an augmentation database component 2914.Referring to FIG. 6 , the augmentation component 2910 operationallyselects and adds augmentations 638, 644, 650, 656 to augmented basemodel 636, augmented simplified base model 642, augmented modified basemodel 648, and augmented simplified modified base model 654,respectfully; the rendering component 2912 takes augmentations 634 andprocesses the augmentations 634 to generate rendered images 628, 664;and, the augmentation database component 2914 operationally performs theoperations of managing the augmentation database 612, which may bedistributed. As illustrated, the processor 2902 is communicativelycoupled to both the processor 2906 and the processor 2908.

GLOSSARY

“Carrier signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Client device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Communication network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleexamples, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering examples in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In examples in which multiple hardware componentsare configured or instantiated at different times, communicationsbetween such hardware components may be achieved, for example, throughthe storage and retrieval of information in memory structures to whichthe multiple hardware components have access. For example, one hardwarecomponent may perform an operation and store the output of thatoperation in a memory device to which it is communicatively coupled. Afurther hardware component may then, at a later time, access the memorydevice to retrieve and process the stored output. Hardware componentsmay also initiate communications with input or output devices, and canoperate on a resource (e.g., a collection of information). The variousoperations of example methods described herein may be performed, atleast partially, by one or more processors that are temporarilyconfigured (e.g., by software) or permanently configured to perform therelevant operations. Whether temporarily or permanently configured, suchprocessors may constitute processor-implemented components that operateto perform one or more operations or functions described herein. As usedherein, “processor-implemented component” refers to a hardware componentimplemented using one or more processors. Similarly, the methodsdescribed herein may be at least partially processor-implemented, with aparticular processor or processors being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented components. Moreover,the one or more processors may also operate to support performance ofthe relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), with these operations being accessiblevia a network (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors, not only residing within asingle machine, but deployed across a number of machines. In someexample examples, the processors or processor-implemented components maybe located in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleexamples, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Computer-readable storage medium” refers to both machine-storage mediaand transmission media. Thus, the terms include both storagedevices/media and carrier waves/modulated data signals. The terms“machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The plural of “computer-readablemedium” may be termed “computer-readable mediums”.

“Ephemeral message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

“Machine storage medium” refers to a single or multiple storage devicesand media (e.g., a centralized or distributed database, and associatedcaches and servers) that store executable instructions, routines anddata. The term shall accordingly be taken to include, but not be limitedto, solid-state memories, and optical and magnetic media, includingmemory internal or external to processors. Specific examples ofmachine-storage media, computer-storage media and device-storage mediainclude non-volatile memory, including by way of example semiconductormemory devices, e.g., erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), FPGA, andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks Theterms “machine-storage medium,” “device-storage medium,”“computer-storage medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms “machine-storage media,”“computer-storage media,” and “device-storage media” specificallyexclude carrier waves, modulated data signals, and other such media, atleast some of which are covered under the term “signal medium.”

“Non-transitory computer-readable storage medium” refers to a tangiblemedium that is capable of storing, encoding, or carrying theinstructions for execution by a machine.

“Signal medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

What is claimed is:
 1. A method comprising: processing a plurality of augmentations to categorize the plurality of augmentations into categories, the categories comprising a gender and a group of people; selecting first augmentations from each of the categories; processing a first three-dimensional (3D) base model to generate a plurality of first images, each first image of the plurality of first images depicting the first 3D base model modified by the first augmentations; determining, for a second 3D base model, incompatible augmentations of the first augmentations, the incompatible augmentations indicating modifications to fixed features of the second 3D base model; and processing the second 3D base model to generate a second image for each of the plurality of first images, each second image depicting the second 3D base model modified by second augmentations, the second augmentations corresponding to the first augmentations of a corresponding first image, wherein the second augmentations comprise augmentations of the first augmentations that are not incompatible augmentations.
 2. The method of claim 1 further comprising: processing the first 3D base model to modify the first 3D base model to generate the second 3D base model.
 3. The method of claim 1 wherein the plurality of augmentations comprises: item augmentations, shader augmentations, blend shape augmentations, lighting augmentations, and orientation augmentations.
 4. The method of claim 1 wherein each category of the categories includes a proxy object associated with the first 3D base model, wherein the first augmentations are applied in accordance with a corresponding proxy object, and wherein the proxy object comprises a plurality of polygons and a location relative to the first 3D base model.
 5. The method of claim 4 wherein the proxy object is a first proxy object, wherein each category of the categories includes a second proxy object associated with the second 3D base model, wherein the second augmentations are applied in accordance with a corresponding second proxy object, and wherein the second proxy object comprises a plurality of polygons and a location relative to the second 3D base model.
 6. The method of claim 1 wherein a matte comprises a plurality of regions each region indicating a portion of the first 3D base model, wherein each category of the categories is associated with a region of the plurality of regions, and wherein the first augmentations are applied to regions of the first 3D base model in accordance with a corresponding region.
 7. The method of claim 1 wherein the fixed features comprise one or more of a skin color, an ear shape, a facial expression, a clothing item, a lighting of the second 3D base model, and a shading of the second 3D base model.
 8. The method of claim 1 wherein the first augmentations are modified for the first 3D base model and the second augmentations are modified for the second 3D base model.
 9. The method of claim 1 wherein the first 3D base model comprises a mesh of polygons with vertices and wherein the first augmentations comprise a morph augmentation that indicates how to modify the vertices of the mesh with the morph augmentation.
 10. The method of claim 9 wherein the morph augmentation is a facial expression or a facial structure indicating a group of people.
 11. The method of claim 1 further comprising: processing the first 3D base model to generate a simplified first 3D base model, wherein the first 3D base model comprises a first plurality of polygons and the simplified first 3D base model comprises a second plurality of polygons, and wherein a number of the first plurality of polygons is greater than a number of the second plurality of polygons; and generating one or more of the plurality of first images from the simplified first 3D base model.
 12. The method of claim 1 wherein the plurality of second images are a ground truth input and the plurality of second images are a ground truth output.
 13. The method of claim 12 further comprising: training a convolutional neural network using the ground truth input and the ground truth output.
 14. The method of claim 1 further comprising: rendering the plurality of first images and the plurality of second images.
 15. A system comprising: one or more computer processors; and one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to perform operations comprising: processing a plurality of augmentations to categorize the plurality of augmentations into categories, the categories comprising a gender and a group of people; selecting first augmentations from each of the categories; processing a first three-dimensional (3D) base model to generate a plurality of first images, each first image of the plurality of first images depicting the first 3D base model modified by the first augmentations; determining, for a second 3D base model, incompatible augmentations of the first augmentations, the incompatible augmentations indicating modifications to fixed features of the second 3D base model; and processing the second 3D base model to generate a second image for each of the plurality of first images, each second image depicting the second 3D base model modified by second augmentations, the second augmentations corresponding to the first augmentations of a corresponding first image, wherein the second augmentations comprise augmentations of the first augmentations that are not incompatible augmentations.
 16. The system of claim 15 wherein the plurality of augmentations comprises: item augmentations, shader augmentations, blend shape augmentations, lighting augmentations, and orientation augmentations.
 17. A non-transitory computer-readable storage medium including instructions that, when processed by a computer, configure the computer to perform operations comprising: processing a plurality of augmentations to categorize the plurality of augmentations into categories, the categories comprising a gender and a group of people; selecting first augmentations from each of the categories; processing a first three-dimensional (3D) base model to generate a plurality of first images, each first image of the plurality of first images depicting the first 3D base model modified by the first augmentations; determining, for a second 3D base model, incompatible augmentations of the first augmentations, the incompatible augmentations indicating modifications to fixed features of the second 3D base model; and processing the second 3D base model to generate a second image for each of the plurality of first images, each second image depicting the second 3D base model modified by second augmentations, the second augmentations corresponding to the first augmentations of a corresponding first image, wherein the second augmentations comprise augmentations of the first augmentations that are not incompatible augmentations.
 18. The non-transitory computer-readable storage medium of claim 17 wherein the plurality of augmentations comprises: item augmentations, shader augmentations, blend shape augmentations, lighting augmentations, and orientation augmentations. 